1. Field of the Invention
This invention relates to data processing for geophysical exploration and is, more specifically, related to simultaneous joint inversion of surface wave and refraction data to obtain the near surface properties in three-dimensional (“3D”) and two-dimensional (“2D”) seismic surveys.
2. Description of the Related Art
This section of this document introduces various aspects of the art that may be related to various aspects of the present invention described and/or claimed below. It provides background information to facilitate a better understanding of the various aspects of the present invention. As the section's title implies, this is a discussion of “related” art. That such art is related in no way implies that it is also “prior” art. The related art may or may not be prior art. The discussion in this section of this document is to be read in this light, and not as admissions of prior art.
Surface waves are seismic events propagating without radiation towards the Earth's interior, parallel to the surface, with a reduced geometric spreading compared to body waves. In land seismic, they carry large part of the energy radiated by a source at the surface. Surface waves constitute most of the coherent noise in seismic data: they are source generated events characterized by low velocity and high amplitude, and they superimpose on to the useful signal. This coherent noise (often simply called ground roll in land seismic) can be made of different wave types, such as Rayleigh waves, with multiple modes of propagation, Lamb waves, P-guided waves, Love waves, Scholte waves.
The propagation properties of surface waves depend on the elastic properties of the so-called near-surface, the shallow portion of the earth which is responsible of most of the perturbation and degradation of the seismic signals. The accurate identification of the properties of the different surface waves is a crucial point for the design of filters (adapted to the different properties), and can be used for the generation of noise models to be subtracted from data. Moreover, since the propagation properties are closely related to the near surface elastic parameters, the analysis of surface wave allows the near surface characterization. The dispersion curve can be inverted to get a velocity profile as demonstrated in Xia J., et al., “Estimation of near-surface shear wave velocity by inversion of Rayleigh waves”, 64 Geophysics 691-700 (1999).
The characterization techniques based on the analysis and inversion of the surface wave properties have been used in different disciplines, from the large scale of the earthquake seismology to the very small scale of ultrasonic non-destructive testing. The surface wave method for the near surface characterization is a three-step process: seismic data are acquired with a linear array of receivers and an in-line source, data are processed to extract the propagation properties, usually the dispersion curve, which is finally inverted to get a single velocity profile associated to one location within the array. A review of the standard approaches is presented by Socco L. V. & Strobbia C., “Surface-wave method for near-surface characterization: a tutorial, Near Surface Geophysics,” 165-185 (2004).
The dispersion curve is often extracted tracking energy maxima in 2D wavefield transforms, in which the energy is mapped from T-X domain into F-K, F-V, F-p. Alternative approaches use the phase difference analysis, such as that disclosed in Strobbia C. & Foti S., “Multi-offset phase analysis of surface wave data (MOPA),” 59/4 Journal of Applied Geophysics, 300-313 (2006), and can identify lateral variations.
The use of one-dimensional forward models makes the method essentially 1D, and the propagation properties are usually extracted neglecting lateral variations. However, extensions to 2D have been discussed in the literature. Most of approaches are based on a moving spatial window. See Bohlen T., et al., “1.5-D Inversion of lateral variation of Scholte Wave dispersion”, 69 Geophysics, 330-344 (2004); Hayashi K., & Suzuki H., “CMP cross-correlation analysis of multi-channel surface-wave data”, 35 Exploration Geophysics 7-13 (2004); Grandjean, G. & A. Bitri, “2M-SASW: Multifold multichannel seismic inversion of local dispersion of Rayleigh waves in laterally heterogeneous subsurfaces: application to the Super-Sauze earthflow, France: Near Surface”, 4 Geophysics, 367-375 (2006).
In the field of earthquake seismology, a three-dimensional model of the Earth's crust and upper mantle can be obtained from the observation of Rayleigh and Love wave dispersion in earthquake data. The group velocity is estimated using multiple filter techniques, Dziewonski, A. M. & Hales A. L., “Numerical analysis of dispersive seismic waves,” 11 Methods in computational physics, 39-85, (B. A. Bolt (ed.) Academic Press 1972), for each source-station path. Alternative approaches can use cross-correlation of ambient noise between couples of stations in a seismic network to estimate the dispersion on interstation paths. Shapiro N. M., et al., “High-Resolution Surface-Wave Tomography from Ambient Seismic Noise,” 307 (5715) Science 1615-1618 (2005).
Even if normally only the velocity is considered, the inversion of the attenuation profile is discussed by Lai C. G., “Simultaneous Inversion of Rayleigh Phase Velocity and Attenuation for Near-Surface Site Characterization”, Ph.D. Dissertation, Georgia Institute of Technology, p. 370 (1998), who extracts attenuation curves from surface wave data and inverts them with a coupled or uncoupled approach.
An example of investigation of the spatial variation of the Rayleigh wave characteristics in 3D land seismic is reported by Ross W. S., et al., “Characterization of spatially varying surface waves in a land seismic survey,” SEG Expanded abstract (2008), the computation of the mean group velocity of Rayleigh wave cross-correlating pairs of signals from adjacent receivers along the same source-receiver azimuth. They estimate a mean attribute of the surface wave between couples of receivers.
Despite some earlier attempts, the systematic application of the surface wave method for entire 3D land surveys, enabled by high quality, broadband point receiver data, is more recent. See, e.g., Strobbia, et al., “Surface Waves: Use Them Then Lose Them”, EAGE conference (2009); Strobbia et al, “Surface Waves: Processing, Inversion and Removal”, First Break, 28, (2010).
The term “refraction” can be defined as the change in the direction of propagation of a wavefront, or the bending of a ray, as it passes from one medium to another. It is expressed mathematically by Snell's law and is consequence of changes in wavelength and velocity of propagation of a wave. It is produced by differences in refractive indices of the media. “Refracted waves” are also called “diving waves” (“DW”) in seismic exploration. Refraction surveys where the incident and reflected angles are critical can be useful for evaluating increasing velocity gradients and locating features that have anomalously high velocities. Currently refraction tomography provides a reliable near-surface velocity model for both structural depth imaging workflows and static corrections for time processing.
The refraction method for the near surface characterization is a three step process: seismic data are acquired with a linear array of receivers and an in line source, data are processed to extract the refraction properties, usually a set of picks of first arrivals on the shot gathers, and then inverted (tomography) to get velocity of the subsurface. The tomography uses refracted first arrivals to compute a near-surface earth model by minimizing the difference between calculated and observed travel times. Because refracted energy samples the near surface with more redundancy and with a greater angular range than reflected waves, it converges robustly to the final velocity model. This approach is used today in land, marine, or OBC environments.
Two-dimensional Simultaneous Joint Inversion (“2D SJI”) is today a robust technology for structural imaging in the exploration framework, mainly used to improve the quality of the velocity models used for depth imaging by means of the integration with different domains (Seismic, Gravity, and Electromagnetic). Two different patent submissions are currently running about SJI for integration of seismic and non seismic data for structural imaging. See U.S. Ser. No. 11/829,551, entitled “Methods and Apparatus for Geophysical Exploration Via Joint Inversion”, filed Jul. 27, 2007, and International Patent Application No. PCT/IT2006/000636, entitled “Method for Building Velocity Models for Pre-Stack Depth Migration via the Simultaneous Joint Inversion of Seismic, Gravity and Magnetotelluric Data”, filed Apr. 9, 2006.
Beyond the algorithm, this requires integrated workflows across traditionally distinct geophysical domains; the SJI approach collects the equations of different domains, sets the links between the models, and inverts for the unknowns using a proper merge of different residuals. The algorithm inverts for all models providing updates for different domains.
The data to be minimized consist of residuals from multiple geophysical domains, cross parameter constraints (empirical, physical, statistical), and external a-priori constraints (e.g., geometrical relations). The objective function for the SJI problem contains the models of different geophysical domains (the unknowns), the data residuals, the single domain regularization and the links, that are simply the constraints between the domains involved.
These terms are relatively weighted to each other and so the structural similarity among models can be weighted more than the relations among parameters (or vice-versa). Constraints (e.g., well logs or geological information) are honored throughout the inversion.
The present invention is directed to resolving, or at least reducing, one or all of the problems mentioned above.